Publications by Author: Joan M. Culley

Objective: The testing of informatics tools designed for use during mass casualty incidents presents a unique problem as there is no readily available population of victims or identical exposure setting. The purpose of this article is to describe the process of designing, planning, and executing a functional exercise to accomplish the research objective of validating an informatics tool specifically designed to identify and triage victims of irritant gas syndrome agents. Materials and Methods: During a 3-year time frame, the research team and partners developed the Emergency Department Informatics Computational Tool and planned a functional exercise to test it using medical records data from 298 patients seen in 1 emergency department following a chlorine gas exposure in 2005. Results: The research team learned valuable lessons throughout the planning process that will assist future researchers with developing a functional exercise to test informatics tools. Key considerations for a functional exercise include contributors, venue, and information technology needs (ie, hardware, software, and data collection methods). Discussion: Due to the nature of mass casualty incidents, testing informatics tools and technology for these incidents is challenging. Previous studies have shown a functional exercise as a viable option to test informatics tools developed for use during mass casualty incidents. Conclusion: Utilizing a functional exercise to test new mass casualty management technology and informatics tools involves a painstaking and complex planning process; however, it does allow researchers to address issues inherent in studying informatics tools for mas casualty incidents. Key words: chlorine exposure, disaster, functional exercise, informatics, mass casualty incident

Background:Chemical exposures pose a significant threat to life. A rapid assessment by first responders and emergency nurses is required to reduce death and disability. Currently, no informatics tools exist to process victims of chemical exposures efficiently. The surge of patients into a hospital emergency department during a mass casualty incident creates additional stress on an already overburdened system, potentially placing patients at risk and challenging staff to process patients for appropriate care and treatment efficacy. Traditional emergency department triage models are oversimplified during highly stressed mass casualty incident scenarios in which there is little margin for error. Emerging mobile technology could alleviate the burden placed on nurses by allowing the freedom to move about the emergency department and stay connected to a decision support system.

Objective:This study aims to present and evaluate a new mobile tool for assisting emergency department personnel in patient management and triage during a chemical mass casualty incident.

Methods:Over 500 volunteer nurses, students, and first responders were recruited for a study involving a simulated chemical mass casualty incident. During the exercise, a mobile application was used to collect patient data through a kiosk system. Nurses also received tablets where they could review patient information and choose recommendations from a decision support system. Data collected was analyzed on the efficiency of the app to obtain patient data and on nurse agreement with the decision support system.

Results:Of the 296 participants, 96.3% (288/296) of the patients completed the kiosk system with an average time of 3 minutes, 22 seconds. Average time to complete the entire triage process was 5 minutes, 34 seconds. Analysis of the data also showed strong agreement among nurses regarding the app’s decision support system. Overall, nurses agreed with the system 91.6% (262/286) of the time when it came to choose an exposure level and 84.3% (241/286) of the time when selecting an action.

Conclusions:The app reliably demonstrated the ability to collect patient data through a self-service kiosk system thus reducing the burden on hospital resources. Also, the mobile technology allowed nurses the freedom to triage patients on the go while staying connected to a decision support system in which they felt would give reliable recommendations.

In this report we examine the effectiveness of WISER in identification of a chemical culprit during a chemical based Mass Casualty Incident (MCI). We also evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks (ANN) using the same experimental conditions as WISER. The reverse engineered set of Signs/Symptoms from the WISER application was used as the training set and 31,100 simulated patient records were used as the testing set. Three sets of simulated patient records were generated by 5%, 10% and 15% perturbation of the Signs/ Symptoms of each chemical record. While all three methods achieved a 100% training accuracy, WISER, BDT and ANN produced performances in the range of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of dimensional reduction using ANN illustrated a dimensional collapse from 79 variables to 40 with little loss of classification performance.